Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/disler/pi-agent-observability/vspecnpx skills add disler/pi-agent-observability --skill vspecgit clone --depth 1 https://github.com/disler/pi-agent-observabilityWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/disler/pi-agent-observability/vspec)<a href="https://agentmods.dev/skills/disler/pi-agent-observability/vspec"><img src="https://agentmods.dev/badge/skills/disler/pi-agent-observability/vspec.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00085 | $0.03136 |
| Opus 5 | $0.00043 | $0.01568 |
| Sonnet 5 | $0.00017 | $0.00627 |
| Haiku 4.5 | $0.00009 | $0.00314 |
Grade A, and why
vspec scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vspec
Purpose
Produce a written engineering implementation plan, then add a visual layer: a hero image plus one diagram per section. Each image is an information-rich, compressed, visual artifact — architecture, nodes, communication flow, data models, lifecycles — not a rendered wall of text. The images are thematically consistent across the whole spec and are linked back into the markdown so the plan reads like an illustrated blueprint.
Two phases, in order:
- Plan phase — build and save the raw markdown plan.
- Image phase — after the plan exists, generate and embed one image per section.
The image phase always runs after the raw plan is written.
Variables
USER_PROMPT: $1
ALL_ARGUMENTS: $ARGUMENTS
PLAN_OUTPUT_DIRECTORY: specs/
IMAGE_GENERATOR: ~/.claude/skills/vspec/scripts/generate_image.py
IMAGE_SIZE: 2048x1152 (wide 16:9 by default)
IMAGE_QUALITY: high
OUTPUT_IMAGE_FORMAT: png
HERO_IMAGE_NAME: 00-hero.png
MAX_TEXT_LABELS_PER_IMAGE: 10
IMAGE_MARKER_PREFIX: vspec
Instructions
Plan phase
- IMPORTANT: If no
USER_PROMPTis provided, stop and ask the user to provide it. - Carefully analyze the user's requirements provided in the USER_PROMPT variable.
- Determine the task type (chore|feature|refactor|fix|enhancement) and complexity (simple|medium|complex).
- Think deeply (ultrathink) about the best approach to implement the requested functionality or solve the problem.
- Explore the codebase to understand existing patterns and architecture.
- Follow the Plan Format below to create a comprehensive implementation plan.
- Include all required sections and conditional sections based on task type and complexity.
- Generate a descriptive, kebab-case filename based on the main topic of the plan.
- Save the complete implementation plan to
PLAN_OUTPUT_DIRECTORY/<descriptive-name>.md. - Ensure the plan is detailed enough that another developer could follow it to implement the solution.
- Include code examples or pseudo-code where appropriate to clarify complex concepts.
- Consider edge cases, error handling, and scalability concerns.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 246 lines · 85 tokens per session scan A 2421a2a912f5
vspec is a skill published in the GitHub repository disler/pi-agent-observability (143 stars, last pushed 3mo ago), licensed MIT. It adds 85 tokens to every session and 3,136 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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